The use of machine based vision systems has always been a computationally intensive task as a large number of pixels need to be processed in order for the acquired image to be processed. This processing requirement arises from the need for high resolution images to be acquired to accurately represent the item represented in the image. The processing capabilities of embedded processing systems that are part of machine based vision systems have required the lowest possible image resolution images to be used.
Previous solutions have either used large and expensive processing systems if the vision systems are required to operate in near-real time or used an off-line processing approach that would not permit the determination of vision based decisions before the next item is examined by the vision system. Off-line systems are not useful in most applications as automating the visual inspection of items typically is desired for a large number of items, such as a sequence of items found on an assembly line.
Typically, an item being inspected will be rotated a random amount relative to the camera field of view (FOV) that is used when a reference image is generated. Because of this fact, the acquired image may not be positioned in a desired orientation relative to the reference image when a correlation or matching operation is to occur. The contents of the reference image may itself be rotated and the processing repeated. However, this rotation and subsequent processing may not be able to adequately discriminate between particular amounts of rotation. This result may depend upon the information content of the reference images. In prior systems, this effect of rotation upon the matching operation has not been considered. The present invention disclosed herein overcomes the above limitations of the prior art machine-based vision systems.